Internet of Things (IoT) devices built on different processor architectures have increasingly become targets of ad- versarial attacks. The recent trends in cross-architecture malware cyber-attacks result in mass compromises in smart environments and IoT malware threat hunting poses a contemporary and complex challenge for researchers. This paper proposes a robust cross-architecture IoT malware threat hunting model based on advanced ensemble learning (MTHAEL) using a novel approach. Our unique MTHAEL model using stacked ensemble of hetero- geneous feature selection algorithms and state-of-the-art neural networks to learn different levels of semantic features demon- strates enhanced IoT malware detection than existing approaches. MTHAEL is the first of its kind that effectively optimizes recur- rent neural network (RNN) and convolutional neural network (CNN) with high classification accuracies and consistently low computational overheads on different IoT architectures. Our proposed MTHAEL is experimentally evaluated comprehensively with a large IoT cross-architecture dataset of 21,137 rare samples and is shown to achieve 99.98% classification accuracy for ARM architecture samples, surpassing prior related works. Cross- architecture bench-marking is also performed on the training of our MTHAEL model with different architectures such as ARM, Intel80386, MIPS, and MIPS+Intel80386 individually. Two different hardware architectures such as Raspberry Pi 4 and Core-i5

Android became more popular and widely used operating system. It has been noticed that the code clones in Android apps make it difficult to maintain the security flaws in source code. To avoid these problems, it is essential to find, identify, evaluate and recover those code clones as early as possible. In this paper, we propose and design DroidCC, a novel clone detection approach in Android applications, that helps to detect different types of clones from APK's source code. A prototype has been developed and implemented on the dataset of almost 30,000 top rated Android apps. DroidCC detects type-1, type-2 and type-3 clones in Android apps at the source code level. It also detects the similar code fragments, that were injected into many applications, which might be an indication of spreading malware. Meanwhile it can detect full and partial level similarity between applications. We evaluate DroidCC clone detection approach on real time data-set and count the Recall and Precision, which is quite significant. Furthermore, our results show that our approach is very efficient and effective in detecting different types of clones to check the similarity level in Android applications.

Cybercrimes are on a dramatic rise worldwide. The crime rate is growing day by day in every field or department which is directly or indirectly connected to the internet including Government, business or any individual. The main objective of this study is to evaluate the vulnerabilities in different software systems at the source code level by tracing their patch files. The authors have collected the source code of different types of vulnerabilities at a different level of granularities. They have proposed different ways to collect or trace the vulnerability code, which can be very helpful for security experts, organisations and software developers to maintain security measures. By following their proposed method, you can build your own vulnerability data‐set and can detect vulnerabilities in any system by using suitable code clone detection technique. The study also includes a discussion of reasons for the rise in cybercrimes including zero‐day exploits. A case study has been discussed with results and research questions to show the effectiveness of this study. This study concludes with the effective key findings of published and non‐published vulnerabilities and the ways to prevent from different security attacks to overcome cybercrimes.

Authentication is the fundamental security service used in almost all remote applications. All such sensitive applications over an open network need authentication mechanism that should be delivered in a trusted way. In this paper, we design an RSA based authentication system for smart IoT environment over the air network using state-of-the-art industry standards. Our system provide security services including X.509 certificate, RSA based Public Key Infrastructure (PKI), challenge/response protocols with the help of proxy induced security service provider. We describe an innovative system model, protocol design, system architecture and evaluation against known threats. Also the implemented solution designed as an add on service for multiple other sensitive applications (smart city apps, cyber physical systems etc.) which needs the support of X.509 certificate based on hard tokens to populate other security services including confidentiality, integrity, non-repudiation, privacy and anonymity of the identities. The proposed scheme is evaluated against known vulnerabilities and given detail comparisons with popular known authentication schemes. The result shows that our proposed scheme mitigate all the known security risks and provide highest level assurance to smart gadgets.